Pulmonary hypertension (PH) in newborns and infants is a complex condition associated with several pulmonary, cardiac, and systemic diseases contributing to morbidity and mortality. Thus, accurate and early detection of PH and the classification of its severity is crucial for appropriate and successful management. Using echocardiography, the primary diagnostic tool in pediatrics, human assessment is both time-consuming and expertise-demanding, raising the need for an automated approach. Little effort has been directed towards automatic assessment of PH using echocardiography, and the few proposed methods only focus on binary PH classification on the adult population. In this work, we present an explainable multi-view video-based deep learning approach to predict and classify the severity of PH for a cohort of 270 newborns using echocardiograms. We use spatio-temporal convolutional architectures for the prediction of PH from each view, and aggregate the predictions of the different views using majority voting. Our results show a mean F1-score of 0.84 for severity prediction and 0.92 for binary detection using 10-fold cross-validation and 0.63 for severity prediction and 0.78 for binary detection on the held-out test set. We complement our predictions with saliency maps and show that the learned model focuses on clinically relevant cardiac structures, motivating its usage in clinical practice. To the best of our knowledge, this is the first work for an automated assessment of PH in newborns using echocardiograms.

Hanna Ragnarsdottir^*, Ece Özkan Elsen^*, Holger Michel^*, Kieran Chin-Cheong, Laura Manduchi, Sven Wellmann^†, Julia E. Vogt^†
* denotes shared first authorship, ^† denotes shared last authorship

International Journal of Computer Vision

06.02.2024

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We propose Tree Variational Autoencoder (TreeVAE), a new generative hierarchical clustering model that learns a flexible tree-based posterior distribution over latent variables. TreeVAE hierarchically divides samples according to their intrinsic characteristics, shedding light on hidden structures in the data. It adapts its architecture to discover the optimal tree for encoding dependencies between latent variables. The proposed tree-based generative architecture enables lightweight conditional inference and improves generative performance by utilizing specialized leaf decoders. We show that TreeVAE uncovers underlying clusters in the data and finds meaningful hierarchical relations between the different groups on a variety of datasets, including real-world imaging data. We present empirically that TreeVAE provides a more competitive log-likelihood lower bound than the sequential counterparts. Finally, due to its generative nature, TreeVAE is able to generate new samples from the discovered clusters via conditional sampling.

Laura Manduchi^*, Moritz Vandenhirtz^*, Alain Ryser, Julia E. Vogt
^* denotes shared first authorship

Spotlight at Neural Information Processing Systems, NeurIPS 2023

20.12.2023

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We propose a new generative hierarchical clustering model that learns a flexible tree-based posterior distribution over latent variables. The proposed Tree Variational Autoencoder (TreeVAE) hierarchically divides samples according to their intrinsic characteristics, shedding light on hidden structures in the data. It adapts its architecture to discover the optimal tree for encoding dependencies between latent variables, improving generative performance. We show that TreeVAE uncovers underlying clusters in the data and finds meaningful hierarchical relations between the different groups on several datasets. Due to its generative nature, TreeVAE can generate new samples from the discovered clusters via conditional sampling.

Laura Manduchi^*, Moritz Vandenhirtz^*, Alain Ryser, Julia E. Vogt
^* denotes shared first authorship

ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling

30.06.2023

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We propose a new generative hierarchical clustering model that learns a flexible tree-based posterior distribution over latent variables. The proposed Tree Variational Autoencoder (TreeVAE) hierarchically divides samples according to their intrinsic characteristics, shedding light on hidden structures in the data. It adapts its architecture to discover the optimal tree for encoding dependencies between latent variables, improving generative performance. We show that TreeVAE uncovers underlying clusters in the data and finds meaningful hierarchical relations between the different groups on several datasets. Due to its generative nature, TreeVAE can generate new samples from the discovered clusters via conditional sampling.

Laura Manduchi^*, Moritz Vandenhirtz^*, Alain Ryser, Julia E. Vogt
^* denotes shared first authorship

ICML 2023 Workshop on Deployment Challenges for Generative AI

30.06.2023

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Spurious correlations are everywhere. While humans often do not perceive them, neural networks are notorious for learning unwanted associations, also known as biases, instead of the underlying decision rule. As a result, practitioners are often unaware of the biased decision-making of their classifiers. Such a biased model based on spurious correlations might not generalize to unobserved data, leading to unintended, adverse consequences. We propose Signal is Harder (SiH), a variational-autoencoder-based method that simultaneously trains a biased and unbiased classifier using a novel, disentangling reweighting scheme inspired by the focal loss. Using the unbiased classifier, SiH matches or improves upon the performance of state-of-the-art debiasing methods. To improve the interpretability of our technique, we propose a perturbation scheme in the latent space for visualizing the bias that helps practitioners become aware of the sources of spurious correlations.

Moritz Vandenhirtz, Laura Manduchi, Ricards Marcinkevics, Julia E. Vogt

Domain Generalization Workshop, ICLR 2023

04.05.2023

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Partitioning a set of elements into subsets of a priori unknown sizes is essential in many applications. These subset sizes are rarely explicitly learned - be it the cluster sizes in clustering applications or the number of shared versus independent generative latent factors in weakly-supervised learning. Probability distributions over correct combinations of subset sizes are non-differentiable due to hard constraints, which prohibit gradient-based optimization. In this work, we propose the differentiable hypergeometric distribution. The hypergeometric distribution models the probability of different group sizes based on their relative importance. We introduce reparameterizable gradients to learn the importance between groups and highlight the advantage of explicitly learning the size of subsets in two typical applications: weakly-supervised learning and clustering. In both applications, we outperform previous approaches, which rely on suboptimal heuristics to model the unknown size of groups.

Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt

ICLR 2023

01.05.2023

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Pulmonary hypertension (PH) in newborns and infants is a complex condition associated with several pulmonary, cardiac, and systemic diseases contributing to morbidity and mortality. Therefore, accurate and early detection of PH is crucial for successful management. Using echocardiography, the primary diagnostic tool in pediatrics, human assessment is both time-consuming and expertise-demanding, raising the need for an automated approach. In this work, we present an interpretable multi-view video-based deep learning approach to predict PH for a cohort of 194 newborns using echocardiograms. We use spatio-temporal convolutional architectures for the prediction of PH from each view, and aggregate the predictions of the different views using majority voting. To the best of our knowledge, this is the first work for an automated assessment of PH in newborns using echocardiograms. Our results show a mean F1-score of 0.84 for severity prediction and 0.92 for binary detection using 10-fold cross-validation. We complement our predictions with saliency maps and show that the learned model focuses on clinically relevant cardiac structures, motivating its usage in clinical practice.

Hanna Ragnarsdottir, Laura Manduchi, Holger Michel, Fabian Laumer, Sven Wellmann, Ece Özkan Elsen, Julia E. Vogt

DAGM German Conference on Pattern Recognition

20.09.2022

DOI

We propose a novel anomaly detection method for echocardiogram videos. The introduced method takes advantage of the periodic nature of the heart cycle to learn three variants of a variational latent trajectory model (TVAE). While the first two variants (TVAE-C and TVAE-R) model strict periodic movements of the heart, the third (TVAE-S) is more general and allows shifts in the spatial representation throughout the video. All models are trained on the healthy samples of a novel in-house dataset of infant echocardiogram videos consisting of multiple chamber views to learn a normative prior of the healthy population. During inference, maximum a posteriori (MAP) based anomaly detection is performed to detect out-of-distribution samples in our dataset. The proposed method reliably identifies severe congenital heart defects, such as Ebstein’s Anomaly or Shone-complex. Moreover, it achieves superior performance over MAP-based anomaly detection with standard variational autoencoders when detecting pulmonary hypertension and right ventricular dilation. Finally, we demonstrate that the proposed method enables interpretable explanations of its output through heatmaps highlighting the regions corresponding to anomalous heart structures.

Alain Ryser, Laura Manduchi, Fabian Laumer, Holger Michel, Sven Wellmann, Julia E. Vogt

The Seventh Machine Learning for Healthcare Conference, MLHC 2022

05.08.2022

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We propose a novel anomaly detection method for echocardiogram videos. The introduced method takes advantage of the periodic nature of the heart cycle to learn different variants of a variational latent trajectory model (TVAE). The models are trained on the healthy samples of an in-house dataset of infant echocardiogram videos consisting of multiple chamber views to learn a normative prior of the healthy population. During inference, maximum a posteriori (MAP) based anomaly detection is performed to detect out-ofdistribution samples in our dataset. The proposed method reliably identifies severe congenital heart defects, such as Ebstein’s Anomaly or Shonecomplex. Moreover, it achieves superior performance over MAP-based anomaly detection with standard variational autoencoders on the task of detecting pulmonary hypertension and right ventricular dilation. Finally, we demonstrate that the proposed method provides interpretable explanations of its output through heatmaps which highlight the regions corresponding to anomalous heart structures.

Alain Ryser, Laura Manduchi, Fabian Laumer, Holger Michel, Sven Wellmann, Julia E. Vogt

Poster at the 2nd Workshop on Interpretable Machine Learning in Healthcare (IMLH), ICML 2022

23.07.2022

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In this work, we study the problem of clustering survival data — a challenging and so far under-explored task. We introduce a novel semi-supervised probabilistic approach to cluster survival data by leveraging recent advances in stochastic gradient variational inference. In contrast to previous work, our proposed method employs a deep generative model to uncover the underlying distribution of both the explanatory variables and censored survival times. We compare our model to the related work on clustering and mixture models for survival data in comprehensive experiments on a wide range of synthetic, semi-synthetic, and real-world datasets, including medical imaging data. Our method performs better at identifying clusters and is competitive at predicting survival times. Relying on novel generative assumptions, the proposed model offers a holistic perspective on clustering survival data and holds a promise of discovering subpopulations whose survival is regulated by different generative mechanisms.

Laura Manduchi^*, Ricards Marcinkevics^*, Michela C. Massi, Thomas Weikert, Alexander Sauter, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Bram Stieltjes, Julia E. Vogt
^* denotes shared first authorship

The Tenth International Conference on Learning Representations, ICLR 2022

25.04.2022

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Constrained clustering has gained significant attention in the field of machine learning as it can leverage prior information on a growing amount of only partially labeled data. Following recent advances in deep generative models, we propose a novel framework for constrained clustering that is intuitive, interpretable, and can be trained efficiently in the framework of stochastic gradient variational inference. By explicitly integrating domain knowledge in the form of probabilistic relations, our proposed model (DC-GMM) uncovers the underlying distribution of data conditioned on prior clustering preferences, expressed as pairwise constraints. These constraints guide the clustering process towards a desirable partition of the data by indicating which samples should or should not belong to the same cluster. We provide extensive experiments to demonstrate that DC-GMM shows superior clustering performances and robustness compared to state-of-the-art deep constrained clustering methods on a wide range of data sets. We further demonstrate the usefulness of our approach on two challenging real-world applications.

Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia E. Vogt

Accepted at NeurIPS 2021

14.12.2021

Survival analysis has gained significant attention in the medical domain with many far-reaching applications. Although a variety of machine learning methods have been introduced for tackling time-to-event prediction in unstructured data with complex dependencies, clustering of survival data remains an under-explored problem. The latter is particularly helpful in discovering patient subpopulations whose survival is regulated by different generative mechanisms, a critical problem in precision medicine. To this end, we introduce a novel probabilistic approach to cluster survival data in a variational deep clustering setting. Our proposed method employs a deep generative model to uncover the underlying distribution of both the explanatory variables and the potentially censored survival times. We compare our model to the related work on survival clustering in comprehensive experiments on a range of synthetic, semi-synthetic, and real-world datasets. Our proposed method performs better at identifying clusters and is competitive at predicting survival times in terms of the concordance index and relative absolute error.

Laura Manduchi^*, Ricards Marcinkevics^*, Julia E. Vogt
^* denotes shared first authorship

Contributed talk at AI for Public Health Workshop at ICLR 2021

09.04.2021

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Generating interpretable visualizations of multivariate time series in the intensive care unit is of great practical importance. Clinicians seek to condense complex clinical observations into intuitively understandable critical illness patterns, like failures of different organ systems. They would greatly benefit from a low-dimensional representation in which the trajectories of the patients’ pathology become apparent and relevant health features are highlighted. To this end, we propose to use the latent topological structure of Self-Organizing Maps (SOMs) to achieve an interpretable latent representation of ICU time series and combine it with recent advances in deep clustering. Specifically, we (a) present a novel way to fit SOMs with probabilistic cluster assignments (PSOM), (b) propose a new deep architecture for probabilistic clustering (DPSOM) using a VAE, and (c) extend our architecture to cluster and forecastclinical states in time series (T-DPSOM). We show that our model achieves superior clustering performance compared to state-of-the-art SOM-based clustering methods while maintaining the favorable visualization properties of SOMs. On the eICU data-set, we demonstrate that T-DPSOM provides interpretable visualizations ofpatient state trajectories and uncertainty estimation. We show that our method rediscovers well-known clinical patient characteristics, such as a dynamic variant of the Acute Physiology And Chronic Health Evaluation (APACHE) score. Moreover, we illustrate how itcan disentangle individual organ dysfunctions on disjoint regions of the two-dimensional SOM map.

Laura Manduchi, Matthias Hüser, Martin Faltys, Julia Vogt, Gunnar Rätsch, Vincent Fortuin

ACM CHIL 2021

04.03.2021

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Echocardiography monitors the heart movement for noninvasive diagnosis of heart diseases. It proves to be of profound practical importance as it combines low-cost portable instrumentation and rapid image acquisition without the risks of ionizing radiation. However, echocardiograms produce high-dimensional, noisy data which frequently proved difficult to interpret. As a solution, we propose a novel autoencoder-based framework, DeepHeartBeat, to learn human interpretable representations of cardiac cycles from cardiac ultrasound data. Our model encodes high dimensional observations by a cyclic trajectory in a lower dimensional space. We show that the learned parameters describing the latent trajectory are well interpretable and we demonstrate the versatility of our model by successfully applying it to various cardiologically relevant tasks, such as ejection fraction prediction and arrhythmia detection. As a result, DeepHeartBeat promises to serve as a valuable assistant tool for automating therapy decisions and guiding clinical care.

Fabian Laumer, Gabriel Fringeli, Alina Dubatovka, Laura Manduchi, Joachim M. Buhmann

best newcomer award + spotlight talk at Machine Learning for Health Workshop, NeurIPS 2020

01.12.2020

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Self-organizing maps (SOMs) have been widely used as a means to visualize latent structure in large amounts of heterogeneous data, in particular as a clustering method. Relatively little work, however, has focused on combining SOMs with deep generative networks for modeling health states, which arise for example in the intensive care unit (ICU). We present Temporal PSOM, a novel neural network architecture that jointly trains a Variational Autoencoder for feature extraction and a probabilistic version of SOM to achieve an interpretable discrete representation of patient health states in the ICU. Experiments on the publicly available eICU data set show significant improvements over state-of-the-art methods in terms of cluster enrichment for current APACHE physiology scores as well as prediction of future physiology states.

Laura Manduchi, Matthias Hueser, Gunnar Raetsch, Vincent Fortuin

ML4H Workshop, NeurIPS 2019

15.12.2019